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Abstract
Background Metabolomics has the characteristics of terminal effects and reflects the physiological state of biological diseases more directly. Several current biomarkers of multiple omics were revealed to be associated with immune-related adverse events (irAEs) occurrence. However, there is a lack of reliable metabolic biomarkers to predict irAEs. This study aims to explore the potential metabolic biomarkers to predict risk of irAEs and to investigate the association of plasma metabolites level with survival in patients with lung cancer receiving PD-1/PD-L1 inhibitor treatment.
Methods The study collected 170 plasmas of 85 patients with lung cancer who received immune checkpoint inhibitors (ICIs) treatment. 58 plasma samples of 29 patients with irAEs were collected before ICIs treatment and at the onset of irAEs. 112 plasma samples of 56 patients who did not develop irAEs were collected before ICIs treatment and plasma matched by treatment cycles to onset of irAEs patients. Untargeted metabolomics analysis was used to identify the differential metabolites before initiating ICIs treatment and during the process that development of irAEs. Kaplan-Meier curves analysis was used to detect the associations of plasma metabolites level with survival of patients with lung cancer.
Results A total of 24 differential metabolites were identified to predict the occurrence of irAEs. Baseline acylcarnitines and steroids levels are significantly higher in patients with irAEs, and the model of eight acylcarnitine and six steroid metabolites baseline level predicts irAEs occurrence with area under the curve of 0.91. Patients with lower concentration of baseline decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0) in plasma would have better overall survival (OS). Moreover, 52 differential metabolites were identified related to irAEs during ICIs treatment, dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine and sphinganine 1-phaosphate were significantly decreased in irAEs group while oxoglutaric acid and taurocholic acid were significantly increased in irAEs group.
Conclusions High levels of acylcarnitines and steroid hormone metabolites might be risk factor to development of irAEs, and levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3 and hexanoylcarnitine (AcCa(6:0) could be used to predict OS for patients with lung cancer received ICIs treatment.
- Lung Cancer
- Biomarker
- Immune Checkpoint Inhibitor
Data availability statement
Data are available on reasonable request.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Biomarkers of multiomics for prediction immune-related adverse events (irAEs) were revealed, such as HLA subtype, cytokines, blood cell counts and ratios, gut microbiome. However, reliable plasma metabolic biomarkers to predict irAEs are still lacking.
WHAT THIS STUDY ADDS
High levels of eight acylcarnitines and six steroids in baseline plasma before received immune checkpoint inhibitors (ICIs) treatment might be risk factors to development of irAEs. Moreover, patients with high levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3, and hexanoylcarnitine (AcCa(6:0) at baseline plasma had worse overall survival for received ICIs treatment.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The study provided potential plasma metabolic biomarkers for prediction of irAEs and prognosis in patients with lung cancer receiving ICIs treatment.
Introduction
Lung cancer remains to have the highest incidence among cancers in men and leading cause of cancer-related deaths worldwide.1 2 Immune checkpoint blockade therapy which targets CTLA-4, PD-1 or PD-L1 has become a major treatment for lung cancer.3 4 Despite their notable successes in improving patients’ survival, their use leads to increased patient vulnerability to immune-related adverse events (irAEs). Due to systemic effects, irAE can affect any organ systems and cause possible life-threatening diseases, such as pneumonitis, myocarditis and cardiomyopathy.5 Therefore, the predictive biomarkers of irAEs are required to determine the benefit/risk ratio for patients receiving anti-PD-1/PD-L1 therapy.
In the search for better biomarkers to detect which patients at risk of irAEs, several groups have recently demonstrated that multivariable models based on clinical data or genetic variation of patients collected prior to treatment can predict response to immune checkpoint inhibitors (ICIs) treatment.6 7 Because of the close phenotypic similarity between patients with irAE and autoimmune disease (AD), host genetic factors that are strong determinants of AD are hypothesized to play an important role in irAE prediction.8 Recently several studies have shown that various irAE are associated with certain HLA alleles and germline variants.8 9 Besides static prediction models, longitudinal data on biomarker fluctuations, such as blood cell counts, autoantibodies and cytokines, may provide a more reliable method for assessing the risk of individuals experiencing irAE. Therefore, results from emerging research based on artificial intelligence, big data and machine learning as methods for creating predictive models of toxicity are particularly promising.10–12
Using untargeted metabolomic profiling to predict irAEs is a novel area of cancer research with the potential to develop methods for measurement of global dynamic changes. Compared with upstream proteomics and genomics, metabolomics has the characteristics of terminal effect and amplification effect. It reflects the physiological state of biological diseases more directly and has higher sensitivity. There have been many publications presenting studies of the role of metabolomics in lung cancer diagnosis.13 14 Only a few studies evaluated the relation between serum metabolomic profiles and response or irAE of immunotherapy in non-small cell lung cancer (NSCLC) patients.15 16 Ghini et al used metabolomics to analyze serum samples from 50 patients with NSCLC treated with ICIs. Significant differences in alanine and Pyruvic acid levels were observed in non-responders and responders of patients treated with nivolumab.17 Dora et al analyzed the stool samples of patients, depicted specific microbial signatures and metabolites that might be associated with the onset and severity of toxicity symptoms.18
In this study, we collected plasma of patients with lung cancer both before and during treatment with ICIs. We aim to investigate the plasma metabolites by using untargeted metabolomic profiling, which could be used to predict irAEs occurrence and survival in patients with lung cancer receiving ICIs treatment.
Materials and methods
Study population and samples
Patients pathologically confirmed of lung cancer who received anti-PD-1/anti-PD-L1 blockade therapy alone or with combined with platinum-based chemotherapy were recruited at Xiangya hospital of Central South University from December 2020 to August 2022. The study participant’s selection is demonstrated in online supplemental figure S1. A total of 85 patients were included in this study, 56 patients without irAEs and 29 patients stopped receiving immunotherapy due to occurrence of irAEs. Seven patients (8.24%) received immunotherapy alone and 78 patients (91.76%) received immunotherapy combined with chemotherapy. Baseline plasma samples were collected before ICIs treatment for all patients. Moreover, patients with irAEs collected plasma samples at the onset of irAEs, and patients without irAEs collected samples according to the treatment cycles to onset of irAEs patients. Patients with irAEs and without irAEs were matched by 1:1 or 1:2 for consideration of age, sex, and stage to confirm the sample of which cycle should be chosen for patients without irAEs. Plasma was separated from 5 mL peripheral blood sample by centrifuge of 4000 rpm at 4℃ and stored at −80℃ until use.
Supplemental material
Data collection and irAEs classification
Clinical data of the patients were collected including age, sex, smoking status, tumor histological type, clinical stage, treatment regimen and cycles. IrAE outcomes were assessed by National Comprehensive Cancer Network (NCCN) guideline of Management of Immunotherapy-related Toxicities, and the severity of irAEs was evaluated by Common Terminology Criteria for Adverse Events (V.5.0).
Plasma metabolites detection
The hydrophilic fraction of metabolite extracts was submitted to untargeted metabolomics analysis platform by using two different analytical methods on an Ultimate 3000 UPLC coupled with Q Exactive quadrupole-Orbitrap mass spectrometer system (Thermo Scientific, San Jose, USA). Reverse phase chromatographic separation mode with positive and negative ionization detection were profiled to extract metabolite for methods 1 and 2, respectively. ACE C18-PFP column (Advanced Chromatography Technologies, Aberdeen, Scotland) was used to separate metabolites for method 1, and the metabolites eluted by 0.1% formic acid in water and acetonitrile using linear gradient ramping from 2% organic mobile phase to 98% in 10 min. Furthermore, other mobile phases consisting of water and ammonium acetonitrile/methanol both containing ammonium bicarbonate buffer salt were employed to eluted metabolites separated on an Acquity HSS C18 column for 110/115 method 2 (Waters Corporation, Milford, USA, 1.8 µm, 2.1×100 mm).
For methods 1 and 2, the quadrupole-Orbitrap mass spectrometer was all operated under identical ionization parameters with a heated electrospray ionization source except ionization voltage including sheath gas 45 arb, aux gas 10 arb, heater temperature 355℃, capillary temperature 320℃ and S-Lens RF level 55%. Full scan mode under 70,000 full width at half maximum (FWHM) resolution with AGC 1E6 and 200 ms max injection time were profiled for metabolome extraction. 70–1000 m/z scan range was acquired. QC samples were repeatedly injected to acquired Top 10 data dependent MS2 spectra (full scan-ddMS2) for comprehensive metabolite and lipid structural annotation. Full MS/MS data acquisition by 17,500 FWHM resolution settings. Apex trigger, dynamic exclusion and isotope exclusion was turned on, precursor isolation window was set at 1.0 Da. Stepped normalized collision energy was employed for collision induced disassociation of metabolite using ultra-pure nitrogen as fragmentation gas. All the data are acquired in profile format.
Data processing and statistical analysis
Compound Discoverer software (Thermo Scientific, San Jose, USA) was used for further procession of the the full scan and data dependent MS2 metabolic profiles data acquired with method 1 and 2 for comprehensive component extraction. By searching acquired MS2 against a proprietary iPhenome SMOL high-resolution MS/MS spectrum local library created using authentic standards as well as mzCloud library (Thermo Scientific, San Jose, USA) for structural annotation of the hydrophilic metabolites. Besides, exact m/z±5 ppm of MS1 spectra was searched against a local HMDB metabolite chemical database.19 Furthermore, structural annotation of metabolites was strictly confirmed by retention time and high-resolution MS/MS spectra similarity. The chemical identification results of metabolites were finally annotated with classification criteria proposed by MSI (lipidomics standardization Initiative).20 MetaboAnalyst V5.0 website (https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml) was used for normalization and further data process.
Student’s t-test and Pearson’s χ2 test were used to determine the differences in clinical characteristics between irAEs and non-irAEs groups. Kaplan-Meier analysis was used to estimate cumulative progression-free survival (PFS) and overall survival (OS), and COX proportional hazards regression analysis was used to exclude the influence of the clinical characteristics. HR was used to evaluate the association of plasma metabolite concentration with prognosis. To define the cut-off value of high/low concentration of plasma metabolites and evaluate the efficacy of the models, receiver-operating characteristic (ROC) curves were drawn by SPSS V.22.0. The cut-off value of normalized peak intensity was chosen by calculating the Youden exponent. To develop models for prediction of irAEs, metabolites significantly related to irAEs were added stepwise into a logistic regression model, ROC curves were drawn and the area under the curve (AUC) with 95% CIs was calculated. All the p values were two sided, and a p<0.05 was considered to be significant.
Results
Study design and participants
To investigate metabolite biomarkers for predicting irAEs from ICIs, we assembled a cohort of 85 patients with advanced lung cancer who received PD-1/PD-L1 blockade. A total of 1189 potentially eligible patients were consecutively enrolled in this study from Xiangya Hospital. 392 patients were excluded because of unable to diagnose lung cancer or did not receive PD-1/PD-L1 blockade inhibitors-based immunotherapy. 669 patients were unavailability of baseline (before initiating immunotherapy) plasma. Finally, 85 patients were included in this study that 56 patients with no irAEs and 29 patients discontinued immunotherapy due to irAEs (online supplemental figure S1). Clinical characteristics of irAE-labeled patient groups (irAE group) and the group of patients without irAEs (non-irAEs group) are summarized in table 1. We identified 10 types of irAEs from 29 patients, pneumonitis was the most frequent irAE type (24%), followed by thyroid dysfunction (19%) and cutaneous toxicities (17%) (figure 1A). There are 9 (31%) patients with two or more types of irAEs (figure 1B). Based on the severity or type of irAEs, “critical irAEs” are defined as leading to discontinuation of immunotherapy, including all grade 3–4 toxicities and some symptomatic types of cardiotoxicities, pulmonary toxicity, gastrointestinal toxicity, hematologic toxicity, endocrine toxicity and renal toxicity (figure 1C). In patients with critical irAEs in our study, the myocarditis, cutaneous toxicities, thyroid dysfunction and diabetes showed early onset time (figure 1D, online supplemental table S1).
Supplemental material
Clinical features of the 85 patients with lung cancer treated with anti-PD1/PD-L1 immunotherapy
Summary of irAEs cohort data. (A, B) Donut plot of patients with irAEs according to irAE types and number of irAE types in patients. (C) Grade of toxicity in patients with different types of irAE. (D) Time to onset of different types of irAE. irAEs, immune-related adverse events.
Identification of potential metabolite biomarkers of irAEs at baseline plasma
For the quality control of the detection, the five QC samples in principal component analysis (PCA) are closely clustered together, indicating that the performance of the experimental platform remains stable during the analysis process; RSD%>30% of the mass spectrum response values account for more than 99%, proving that the data quality is stable and reliable (online supplemental figure S2). To explore the difference in metabolomic profiles between non-irAE group and irAE group, we collected plasma samples from the patients before they received immunotherapy. Analysis of baseline plasma samples screened a total of 24 differential metabolites which were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA) clustering. It showed clustering within each group and significant differences between the groups, indicating a clear separation between the two groups, and patients with irAEs may have similar metabolic characteristics (figure 2A). A subsequent classification was performed for the 24 differentiated metabolites, resulting in identification of acylcarnitines (34%), steroids (25%), fatty acids (13%), amino acids (8%), bile acids (8%), peptides (4%), lysophospholipids (4%), and microbial metabolites (4%) (figure 2B). We observed that most of the significant differentiated metabolites were upregulated in irAEs group, the most significantly upregulated metabolite was glutaryl carnitine (AcCa (5:0-DC) while the most significantly downregulated metabolite was eicosanedioic acid (FFA (20:0-DC) (figure 2C). Among acylcarnitine and steroid metabolites with p value less than 0.1, there were 19 acylcarnitine metabolites and 7 steroid metabolites upregulated in irAEs group, respectively (figure 2D,E).
Supplemental material
Metabolite biomarkers of irAEs at baseline plasma. (A) Distribution of samples according to principal component analysis. (B) Proportion of different types of 24 differentiated metabolites. (C) Flod change of differentiated metabolites. (D) Radar plot of 19 acylcarnitines by normalized plasma levels in irAEs group and non-irAEs group (E) Radar plot of 7 steroids by normalized plasma levels in irAEs group and non-irAEs group. irAEs, immune-related adverse events.
There were eight acylcarnitine metabolites (butyrylcarnitine(AcCa(40), decenoylacrnitine(AcCa(10:1)2, decenoylcarnitine(AcCa(10:1)3, decenoylacrnitine(AcCa(10:1), glutarylcarnitine(AcCa(5:0-DC), hexanoylcarnitine(AcCa(6:0), isobutyrylcarnitine(AcCa(iso4:0), isovalerylcarnitine(AcCa(iso5:0)) and six steroid metabolites (5alpha-pregnan-3beta,20alpha-diol disulfate, allopregnanolone sulfate, pregnandiol sulfate(2), pregnanediol monosulfate, prenanediol O-glucuronide, pregnanolone sulfate) were significantly higher in irAEs group than non-irAEs group (figure 3A,B). By using logistic regression, we trained irAEs prediction models using the 8 acylcarnitine metabolites or the 6 steroid metabolites or combined the 14 metabolites. The AUC for the prediction of irAEs was 0.76, 0.72 and 0.91, respectively (figure 3C).
(A) Normalized plasma levels of eight differentiated acylcarnitines in the two groups. (B) Normalized plasma levels of six differentiated steroids in the two groups. (C) ROC curve of eight acylcarnitine model, six steroids model and combined model to predict irAEs. AUC, area under the curve; irAEs, immune-related adverse events; ROC, receiver operating characteristic.
Identification of irAEs-related metabolites during ICIs treatment
To investigate the changes in plasma metabolic profile during the process that development of irAEs, we analyzed the plasma metabolites of the patients with irAEs on both the baseline plasma and the plasma collected on the time onset of irAEs, while patients with non-irAEs analyzed the baseline plasma and the plasma matched by treatment cycles to onset of irAEs in irAEs group. The ratio of treatment/baseline for each metabolite was used to analyze the metabolites significantly changed for development of irAEs. A total of 648 metabolites were conducted with differentiation analysis, and 52 differential metabolites with p value less than 0.05 were subjected to POLS-DA clustering. It showed a clear separation between the two groups (figure 4A, B), indicating that the metabolic changes in irAEs group were different from those in the non-irAEs group. Among the 52 metabolites, steroids accounts for 33% and followed by amino acids (15%) and peptides (13%) (online supplemental figure S3). Most of the metabolites were downregulated in irAEs group in the top 20 differentiated metabolites (figure 4C). For KEGG pathway analysis of the differentiated metabolites, the results showed that seven metabolites were involved in 13 metabolic pathways. Dehydroepiandrosterone sulfate, corticosterone and cortisol were related to steroid hormone biosynthesis. Taurocholic acid was involved in both primary bile acid biosynthesis and taurine and hypotaurine metabolism pathways. Thyroxine was related to tyrosine metabolism. Sphinganine 1-phosphate was in sphingolipid metabolism, and oxoglutaric acid was associated with multiple metabolic pathways (figure 4D, online supplemental table S2). Dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine and sphinganine 1-phaosphate were significantly decreased in irAEs group while oxoglutaric acid and taurocholic acid were significantly increased in irAEs group (figure 4E). It means that patients with lung cancer with decreased dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine and sphinganine 1-phaosphate or with increased oxoglutaric acid and taurocholic acid during ICIs treatment would have more risk for occurrence of irAEs.
Supplemental material
Supplemental material
Metabolite changes related to irAEs during ICIs treatment. (A) Distribution of samples according to principal component analysis. (B) Heatmap of differentiated metabolites. (C) Fold change of top 20 metabolites. (D) KEGG pathway analysis of differentiated metabolites. (E) Differentiated changed metabolites in top 10 pathways in irAEs group and non-irAEs group. ICIs, immune checkpoint inhibitors; irAEs, immune-related adverse events.
Baseline acylcarnitines associated with prognosis of patients with lung cancer received ICIs treatment
Lots of studies reported that ICIs response was related to irAEs, tumor patients with moderate irAEs would have better treatment response. In our study, the PFS was not related to irAEs, and OS was better in patients of non-irAEs group in our study (figure 5A,B). It might be the reason that the samples in irAEs group were screened with irAEs that lead to the patients discontinued immunotherapy, which contributed to worse survival of the patients. To further investigate whether the metabolites which associated with irAEs could be biomarkers to predict OS, we conducted association analyses between metabolites which associated with irAEs in baseline plasma and OS of the 85 patients. The results showed that patients with lower concentrations of decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0) in baseline plasma would have better OS, respectively (figure 5C, table 2, online supplemental figure S4).
Supplemental material
Association of OS with baseline plasma metabolites level
Kaplan-Meier curves analyses for irAEs and differentiated metabolites level at baseline. (A) Progression-free survival analysis of irAEs. (B) Overall survival analysis of irAEs. (C) Kaplan-Meier curves of metabolites plasma level at baseline significantly related to OS. irAEs, immune-related adverse events; OS, overall survival.
Discussion
This study performed untargeted metabolomics in 170 plasma samples of 85 patients with lung cancer who received ICIs treatment and evaluated the association between 655 metabolites with irAEs. Most metabolites associated with irAEs were acylcarnitines and steroids in baseline plasma samples. Patients with lung cancer with high concentration of eight acylcarnitines (ie, butyrylcarnitine(AcCa(40), decenoylcarnitine(AcCa(10:1)2, decenoylcarnitine(AcCa(10:1)3, decenoylcarnitine(AcCa(10:1), glutarylcarnitine(AcCa(5:0-DC), hexanoylcarnitine(AcCa(6:0), isobutyrylcarnitine(AcCa(iso4:0), isovalerylcarnitine(AcCa(iso5:0)) and six steroids (ie, 5alpha-pregnan-3beta,20alpha-diol disulfate, allopregnanolone sulfate, pregnandiol sulfate(2), pregnanediol monosulfate, prenanediol O-glucuronide, pregnanolone sulfate) in baseline plasma were significantly related to occurrence of irAEs. By comparison of plasma metabolism of before and after patients received immunotherapy, dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine, sphinganine 1-phaosphate, oxoglutaric acid and taurocholic acid were significantly changed during the development of irAEs. Moreover, patients with lower concentrations of decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0) in baseline plasma would have better OS.
Acylcarnitine is a class of carnitine containing acyl-chains, its physiological function is due to the extra acyl chain compared with ordinary carnitine. According to the length of the carbon chain, the acylcarnitines divided into four groups based on the number of carbon atoms in the acyl chain: short-chain (C2-C5), medium-chain (C6-C12), long-chain (C13-C20) and very long-chain (C21-).21 Short-chain acylcarnitine is the main acylcarnitine in plasma, and abnormal short-chain acylcarnitine is associated with multiple types of diseases, including metabolic disease, cardiovascular disease and tumor.22–24 Medium-chain acylcarnitines are usually derived from the corresponding medium-chain fatty acids by esterification with L-carnitine, and altered medium-chain acylcarnitine concentrations contribute to diabetes, cardiovascular disease, and cancer.25–28 Long-chain acylcarnitines were generated by L-carnitine esterification with long-chain acids obtained from diet or lipogenesis. The main function of long-chain acylcarnitine is to ensure long-chain fatty acid transport into the mitochondrial, and altered concentrations of circulating long-chain acylcarnitines are related to various cardiovascular diseases.29 30 Very long-chain fatty acids were too long to participate in mitochondria β-oxidation, it metabolized in peroxisomes and then cleaved into shorter fragments that form shot-chain or medium-chain acylcarnitines to be involved in β-oxidation.31 In patients with peroxisomal enzyme defects or alerted biogenesis of peroxisomes, the very long-chain acylcarnitines can be accumulated.32 33 In our study, acylcarnitines which with higher concentration in baseline plasma that are associated with occurrence of irAEs are short-chain and medium-chain acylcarnitines. Moreover, three medium-chain acylcarnitines (decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0)) related to OS of patients with lung cancer received immunotherapy. There are many methods for detection of acylcarnitine, including high-performance liquid chromatography, mass spectrometry (MS), fluorescence spectrometry, for testing total acylcarnitine, acylcarnitine pairs, and acylcarnitine of different fatty acids. Detection of acylcarnitine is contributed to diagnose the risk factors associated with neurological and cardiovascular disease, such as Parkinson’s disease, Alzheimer’s disease, heart failure. It also can be used to evaluate the effect of drugs on fatty acid metabolism for guiding drug development.
Acylcarnitine is involved in fatty acid metabolism, transporting organic acids and fatty acids from the cytoplasm to the mitochondria for decomposition to produce energy.21 Studies have shown that acylcarnitines are associated with inflammation which demonstrated that L-acylcarnitines induced expression of COX-2 depending on acyl chain length, L-C16 and L-C18 acylcarnitine induced the highest expressions.34 Moreover, it promoted the secretion of TH17-related cytokines in PBMC by affecting mitochondrial β oxidation, thus leading to inflammation.35 Moreover, acylcarnitine participates in a variety of immune reactions in cells, such as T cell proliferation, B cell activation, and macrophage phagocytosis. Acylcarnitines could be used as signaling molecule to regulate intracellular signaling pathway to promote cell proliferation, differentiation, apoptosis, and other immune functions. For example, acylcarnitines promote T cell proliferation by activating intracellular signaling pathway and promote phagocytosis and elimination of pathogens by macrophages. Therefore, acylcarnitine is considered to be an important immunomodulatory substance. In our study, multiple acylcarnitines were higher in plasma of patients with irAEs compared with patients with non-irAEs. We hypothesized that patients with higher levels of plasma acylcarnitines were more likely to have adverse effects because acylcarnitines promote the immune response. Thus, when patients receive immunotherapy, they might be more easily active abundance immune response which results in increased susceptibility to irAEs. However, further studies are needed to investigate the relationship between acylcarnitine and immune function and the mechanisms.
Our study revealed that baseline steroid hormone metabolites are associated with irAEs occurrence. There were many studies that demonstrated that sex might be one of the factors that affect immunotherapy response and irAEs. Males showed larger survival benefit than females with twice of risk reduction in males compared with females,36 and females are more likely to develop irAEs than males, particularly pneumonitis and endocrinopathies.37 Moreover, menopausal status in females also influence the risk of developing irAEs, premenopausal females had more risk than both postmenopausal females and males.37 These indicated that patients with higher steroid hormone metabolite levels are prevalent in irAEs, which was consistent with findings in our study. Females are more enriched for innate and adaptive immune cells in tumor microenvironment of NSCLC, but they developed more complex mechanisms of resistance by greater exhaustion status of CD4+ and CD8+ T cells, higher abundance of immune-suppressive cells, and higher expression of inhibitory immune checkpoint molecules.38 The sex modulates the molecular mechanisms of anti-tumor immune response, which in turns affects the efficacy and toxicity of ICIs. Pala et al considered that research linking sex hormones to sex dimorphism of anticancer immunity is crucial because hormonal treatment can be easily implemented as a modulator of response or toxicity to immunotherapy.39 Our study revealed that 5alpha-pregnan-3beta,20alpha-diol disulfate, allopregnanolone sulfate, pregnandiol sulfate (2), pregnanediol monosulfate, prenanediol O-glucuronide, and pregnanolone sulfate were significantly higher at baseline in irAEs group. Moreover, plasma steroid hormone metabolites (dehydroepiandosterone sulfate, corticosterone) decreased significantly in patients with irAEs during immunotherapy. However, the mechanisms of these steroid hormone metabolites effect on irAEs need to be further investigation.
To our knowledge, an effective predictive model for irAEs by plasma metabolites is still lacking. We found that combined the eight acylcarnitines and six steroid metabolites which significantly related irAEs occurrence in our study, had good accuracy in predicting irAEs with AUC of 0.91. The patients in the irAEs group were those who had stopped immunotherapy due to toxicity of ICIs treatment, including immune-related pneumonia, enteritis, myocarditis, and severe cutaneous toxicity and thyroid dysfunction. Cutaneous toxicity and thyroid dysfunction are the most common types of side effects in the patients with irAEs, but the degree of the two types of irAEs is mild in most patients. There are lots of studies reported that patients with lung cancer with irAEs had better OS and PFS,40–42 but our study showed the reverse result that patients with irAEs had worse OS. This might be the reason that patients who had irAEs in our study discontinued immunotherapy, which affected the clinical treatment and OS of patients. However, patients with mild irAEs would continue to receive immunotherapy and benefit from the treatment. We found that levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3, and hexanoylcarnitine (AcCa(6:0) at baseline plasma were related to OS of patients with lung cancer received ICIs, patients with lower concentrations of the three metabolites had better OS. Thus, the three metabolites might be used as biomarkers for prediction survival of patients with lung cancer receiving immunotherapy.
There were several limitations in our study. Validation should be conducted in independent samples and absolute cut-off concentration for the metabolites to predict irAEs occurrence should be defined. Moreover, roles and mechanisms of acylcarnitine and steroid hormone metabolites act on functions of immune cells need to be further investigated. The sample size is not large enough due to our study design because only patients who discontinued immunotherapy were recruited in irAEs group, and the plasmas both before and after immunotherapy were used. There are two reasons for choosing the extremely samples, one is that we hypothesize that metabolites level changed greater in patients with severe irAEs than patients with mild irAEs, the other is that discover the metabolites associated with irAEs-related discontinuation of immunotherapy has more clinical significance. Multiple-testing correction was not performed due to no metabolite remaining significant, it might be because the sample size for the study was not large enough.
In conclusion, high levels of acylcarnitines and steroid hormone metabolites in plasma before received ICIs treatment might be risk factor to development of irAEs, and patients with high levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3, and hexanoylcarnitine (AcCa(6:0) at baseline plasma had worse OS for received ICIs treatment.
Data availability statement
Data are available on reasonable request.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants and the study protocol was approved by the Ethics Committee of Xiangya hospital, Central South University (2022100970). Participants gave informed consent to participate in the study before taking part.
References
Supplementary materials
Supplementary Data
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Footnotes
JC, J-SL and J-YL contributed equally.
Contributors All authors contributed to the article and approved the submitted and revised version. Z-QL and ZW are the guarantors of the study. JC, ZW and Z-QL conceived of and designed the study. J-SL, LS, TZ and X-PL collected plasma samples and data from electronic medical records. JC, J-SL and J-YL performed the experiments and analyzed the data. JC, J-SL and ZW wrote the manuscript and prepared all figures and tables. J-YL, Z-QL and FY edited the manuscript.
Funding This work was supported by the National Natural Science Foundation of China (82173901), Major Project of Natural Science Foundation of Hunan Province (Open competition, 2021JC0002), Hunan Province key research and development plan of China (2023SK2007), China Postdoctoral Science Foundation (2023M733973), Natural Science Foundation of Hunan Province (2021JJ40325, 2024JJ8180), and Changsha Municipal Natural Science Foundation (kq2014208, kq2208408).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.